Dual adaptive training of photonic neural networks

• URL: http://arxiv.org/abs/2212.06141v1
• Date: Fri, 9 Dec 2022 05:03:45 GMT
• Title: Dual adaptive training of photonic neural networks
• Authors: Ziyang Zheng, Zhengyang Duan, Hang Chen, Rui Yang, Sheng Gao, Haiou Zhang, Hongkai Xiong, Xing Lin
• Abstract summary: Photonic neural network (PNN) computes with photons instead of electrons to feature low latency, high energy efficiency, and high parallelism. Existing training approaches cannot address the extensive accumulation of systematic errors in large-scale PNNs. We propose dual adaptive training ( DAT) that allows the PNN model to adapt to substantial systematic errors.
• Score: 30.86507809437016
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Photonic neural network (PNN) is a remarkable analog artificial intelligence (AI) accelerator that computes with photons instead of electrons to feature low latency, high energy efficiency, and high parallelism. However, the existing training approaches cannot address the extensive accumulation of systematic errors in large-scale PNNs, resulting in a significant decrease in model performance in physical systems. Here, we propose dual adaptive training (DAT) that allows the PNN model to adapt to substantial systematic errors and preserves its performance during the deployment. By introducing the systematic error prediction networks with task-similarity joint optimization, DAT achieves the high similarity mapping between the PNN numerical models and physical systems and high-accurate gradient calculations during the dual backpropagation training. We validated the effectiveness of DAT by using diffractive PNNs and interference-based PNNs on image classification tasks. DAT successfully trained large-scale PNNs under major systematic errors and preserved the model classification accuracies comparable to error-free systems. The results further demonstrated its superior performance over the state-of-the-art in situ training approaches. DAT provides critical support for constructing large-scale PNNs to achieve advanced architectures and can be generalized to other types of AI systems with analog computing errors.

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